Conservation decision makers must negotiate social and technical complexities to achieve desired biodiversity outcomes. Quantitative models can inform decision making, by evaluating and predicting management outcomes, so that comparisons can be made between alternative courses of action. However, whether a proposed action is appropriate for implementation, regardless of its contribution to management outcomes, also requires consideration. Existing quantitative models have yet to fully incorporate the suitability of proposed management actions, which hinders their ability to inform decision making. We used gradient boosted decision trees – a machine‐learning technique – to determine the suitability of alternative management actions available to a biodiversity conservation programme. We demonstrate our approach using the Predator Free 2050 programme – a large and complex conservation initiative that seeks to eradicate selected invasive vertebrates from the entirety of New Zealand by 2050. We created a nationally contiguous network of management tools to suppress populations of invasive species across the entire country. We then used our suitability predictions to explore three scenarios for selecting invasive species management tools, based on maximising (a) implementation probability, (b) humaneness and (c) cost‐savings. Our models highlighted that an interplay of factors influence where management tools can potentially be implemented. Our management scenarios revealed what different contiguous management networks could look like for New Zealand over the next 10–15 years as an interim step to achieving Predator Free 2050. Each scenario differed in the tools selected for implementation in different places and in the overall economic costs associated with creating a contiguous management network. Some locations were identified as unsuitable for any existing management tools, indicating that future transformative technologies may be required to create a contiguous network. Synthesis and applications. Conservation decision making must not only consider biodiversity outcomes but also whether selected management actions are appropriate in the first place. Here, we used machine‐learning techniques to determine the suitability of competing managements actions that are proposed to meet biodiversity objectives. Our approach provides an objective, transparent and reproducible framework to determine the suitability of actions at sites across large spatial extents, under complex social and technical constraints. Whakarāpopototanga Kia whakatutuki i ngā putanga kanorau koiora, arā anō ngā piki me ngā heke ā‐pāpori, ā‐hangarau hoki hei urungi mā ngā mana whakatau i te atawhainga o te ao tūroa nei. Auau tonu nei te whakamahinga o ngā tauira ine rahi ki te tautoko i ngā whiriwhiri whakataunga, mā te matapae i ngā putanga whakahaere, kia taea ai ngā whakatauritenga i waenga i ngā momo kōkiritanga mahi. Heoi anō, ahakoa kua tika rānei te mahi kua whakatakoto mō te whakatinanatanga, ahakoa hoki te pitomata o taua mahi ki te tautoko i ngā putanga whakahaere, me aromatawai tonu. Kāore anō kia kōkuhua katoatia e ngā tauira ine rahi onāianei te hāngaitanga o ngā mahi whakahaere kua whakatakoto, he mea whakaroiroi i tā rātou āhei ki te tautoko i te mahi whakatau. I whakamahia e mātou ngā rākau whakapiki whakatau ā‐ronaki ‐ he āhuatanga ako‐ā‐pūrere ‐ i te whakamahinga āhua rerekē nei ki te whiriwhiri i te hāngaitanga o ngā mahi whakahaere kē kua whakatakoto mō tētahi kaupapa atawhai nuku kanorau koiora. Ka whakaaturia e mātou te ngā painga o tō mātou momo kōkiri e whakamahi ana i te kaupapa Predator Free 2050 ‐ he kaupapa kōkiri atawhai nuku nui, matatini anō hoki e whai ana ki te whakakore atu i ētahi momo kararehe urutomo i Aotearoa nei i mua i te tau 2050. Tā mātou i konei he aro ki te hanga whatunga haere tonu ā‐motu o ngā utauta whakahaere ki te kaupēhi, tāmi hoki i ngā tini kararehe urutomo puta katoa i te whenua. Kātahi ka whakamahia e mātou ā mātou matapae hāngaitanga ki te tūhura i ngā wheako whakaari i whakapikihia takitahitia nei i (i) te tūponotanga whakatinanatanga, (ii) te ngākau atawhaitanga, ka mutu, (iii) te pai o te utu, he take matua ēnei kua whakaarohia e ngā mana whakatau i te tīpakotanga o ngā utauta whakahaere kararehe urutomo. He mea whakapuaki ō mātou tauira i te whitiwhitinga matatini o ngā take e whiriwhiri ki hea pea whakatinanahia ai ngā utauta whakahaere. I whakakitea e ō mātou wheako whakaari whakahaere he pēhea te āhua o ngā whatunga whakahaere haere tonu i Aotearoa i roto i te 10 ki te 15 tau e tū mai nei. He rerekē ia wheako whakaari i ngā utauta i tīpakona mō te whakatinanatanga ki tēnā wāhi, ki tēnā wāhi, i ngā utu whai pānga hoki ki te hanganga o tētahi whatunga whakahaere haere tonu. I tautuhia ētahi wāhi kāore e pai mō ngā utauta whakahaere onāianei, e tohu ana ka hiahiatia pea ngā momo hangarau panonitanga o nga rā ki tua ki te hanga i te whatunga haere tonu. Te kōtuitui me ngā whakamahinga. I whakatauria e mātou te hāngaitanga o ngā momo mahi whakahaere ki te whakatutuki i ngā whāinga atawhai nuku kua whakatakoto mā te whakamahi i ngā āhuatanga ako‐ā‐pūrere. Ko te āhua o tā mātou momo kōkiri he whakatakoto i te poutarāwaho hei whiriwhiri i te hāngaitanga o ngā momo mahi ki tētahi wāhi ahakoa ko tēhea puta noa i ngā whānuitanga mokowā nui, i raro hoki i ngā herenga a‐pāpori, ā‐hangarau matatini rawa anō. Conservation decision making must not only consider biodiversity outcomes but also whether selected management actions are appropriate in the first place. Here, we used machine‐learning techniques to determine the suitability of competing managements actions that are proposed to meet biodiversity objectives. Our approach provides an objective, transparent and reproducible framework to determine the suitability of actions at sites across large spatial extents, under complex social and technical constraints.